Overview

Dataset statistics

Number of variables16
Number of observations2.098
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory262.4 KiB
Average record size in memory128.1 B

Variable types

Categorical2
Numeric14

Alerts

country has a high cardinality: 165 distinct values High cardinality
score is highly correlated with gdp and 6 other fieldsHigh correlation
gdp is highly correlated with score and 4 other fieldsHigh correlation
social_support is highly correlated with score and 4 other fieldsHigh correlation
hle is highly correlated with score and 4 other fieldsHigh correlation
freedom is highly correlated with score and 2 other fieldsHigh correlation
positive_affect is highly correlated with score and 2 other fieldsHigh correlation
rounded_score is highly correlated with score and 6 other fieldsHigh correlation
scaled_hle is highly correlated with score and 4 other fieldsHigh correlation
score is highly correlated with gdp and 6 other fieldsHigh correlation
gdp is highly correlated with score and 4 other fieldsHigh correlation
social_support is highly correlated with score and 4 other fieldsHigh correlation
hle is highly correlated with score and 4 other fieldsHigh correlation
freedom is highly correlated with score and 1 other fieldsHigh correlation
positive_affect is highly correlated with score and 2 other fieldsHigh correlation
rounded_score is highly correlated with score and 5 other fieldsHigh correlation
scaled_hle is highly correlated with score and 4 other fieldsHigh correlation
score is highly correlated with gdp and 4 other fieldsHigh correlation
gdp is highly correlated with score and 4 other fieldsHigh correlation
social_support is highly correlated with score and 2 other fieldsHigh correlation
hle is highly correlated with score and 3 other fieldsHigh correlation
rounded_score is highly correlated with score and 4 other fieldsHigh correlation
scaled_hle is highly correlated with score and 3 other fieldsHigh correlation
region is highly correlated with score and 8 other fieldsHigh correlation
score is highly correlated with region and 9 other fieldsHigh correlation
gdp is highly correlated with region and 10 other fieldsHigh correlation
social_support is highly correlated with region and 8 other fieldsHigh correlation
hle is highly correlated with region and 7 other fieldsHigh correlation
freedom is highly correlated with score and 6 other fieldsHigh correlation
generosity is highly correlated with gdp and 1 other fieldsHigh correlation
corruption is highly correlated with region and 5 other fieldsHigh correlation
positive_affect is highly correlated with score and 5 other fieldsHigh correlation
negative_affect is highly correlated with positive_affectHigh correlation
cat_region is highly correlated with region and 10 other fieldsHigh correlation
cat_country is highly correlated with region and 2 other fieldsHigh correlation
rounded_score is highly correlated with region and 8 other fieldsHigh correlation
scaled_hle is highly correlated with region and 7 other fieldsHigh correlation
score has unique values Unique
cat_region has 321 (15.3%) zeros Zeros

Reproduction

Analysis started2022-07-23 17:08:14.075740
Analysis finished2022-07-23 17:08:40.423836
Duration26.35 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

country
Categorical

HIGH CARDINALITY

Distinct165
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
congo
 
19
tajikistan
 
16
lithuania
 
16
saudi arabia
 
16
turkey
 
16
Other values (160)
2015 

Length

Max length25
Median length23
Mean length8.229742612
Min length4

Characters and Unicode

Total characters17.266
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowafghanistan
2nd rowafghanistan
3rd rowafghanistan
4th rowafghanistan
5th rowafghanistan

Common Values

ValueCountFrequency (%)
congo19
 
0.9%
tajikistan16
 
0.8%
lithuania16
 
0.8%
saudi arabia16
 
0.8%
turkey16
 
0.8%
denmark16
 
0.8%
thailand16
 
0.8%
dominican republic16
 
0.8%
ecuador16
 
0.8%
egypt16
 
0.8%
Other values (155)1935
92.2%

Length

2022-07-23T14:08:40.539352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united46
 
1.8%
china42
 
1.7%
south36
 
1.4%
republic34
 
1.4%
of26
 
1.0%
north22
 
0.9%
cyprus22
 
0.9%
congo19
 
0.8%
and19
 
0.8%
uruguay16
 
0.6%
Other values (176)2233
88.8%

Most occurring characters

ValueCountFrequency (%)
a2862
16.6%
i1646
 
9.5%
n1547
 
9.0%
e1240
 
7.2%
r1109
 
6.4%
o983
 
5.7%
s782
 
4.5%
t768
 
4.4%
l730
 
4.2%
u657
 
3.8%
Other values (18)4942
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16813
97.4%
Space Separator417
 
2.4%
Other Punctuation36
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2862
17.0%
i1646
 
9.8%
n1547
 
9.2%
e1240
 
7.4%
r1109
 
6.6%
o983
 
5.8%
s782
 
4.7%
t768
 
4.6%
l730
 
4.3%
u657
 
3.9%
Other values (16)4489
26.7%
Space Separator
ValueCountFrequency (%)
417
100.0%
Other Punctuation
ValueCountFrequency (%)
.36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16813
97.4%
Common453
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2862
17.0%
i1646
 
9.8%
n1547
 
9.2%
e1240
 
7.4%
r1109
 
6.6%
o983
 
5.8%
s782
 
4.7%
t768
 
4.6%
l730
 
4.3%
u657
 
3.9%
Other values (16)4489
26.7%
Common
ValueCountFrequency (%)
417
92.1%
.36
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2862
16.6%
i1646
 
9.5%
n1547
 
9.0%
e1240
 
7.2%
r1109
 
6.4%
o983
 
5.7%
s782
 
4.5%
t768
 
4.4%
l730
 
4.2%
u657
 
3.8%
Other values (18)4942
28.6%

region
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
sub-saharan africa
463 
asia
321 
latin amer. and carib
309 
western europe
292 
eastern europe
196 
Other values (6)
517 

Length

Max length21
Median length18
Mean length14.2135367
Min length4

Characters and Unicode

Total characters29.820
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowasia
2nd rowasia
3rd rowasia
4th rowasia
5th rowasia

Common Values

ValueCountFrequency (%)
sub-saharan africa463
22.1%
asia321
15.3%
latin amer. and carib309
14.7%
western europe292
13.9%
eastern europe196
9.3%
c.w. of ind. states182
 
8.7%
near east171
 
8.2%
northern africa56
 
2.7%
baltics46
 
2.2%
northern america32
 
1.5%

Length

2022-07-23T14:08:40.686494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
africa519
10.9%
europe488
 
10.2%
sub-saharan463
 
9.7%
asia321
 
6.7%
latin309
 
6.5%
amer309
 
6.5%
and309
 
6.5%
carib309
 
6.5%
western292
 
6.1%
eastern196
 
4.1%
Other values (10)1266
26.5%

Most occurring characters

ValueCountFrequency (%)
a5195
17.4%
r2955
9.9%
e2935
9.8%
2683
9.0%
s2316
 
7.8%
n2128
 
7.1%
i1748
 
5.9%
t1466
 
4.9%
c1118
 
3.7%
u951
 
3.2%
Other values (11)6325
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25819
86.6%
Space Separator2683
 
9.0%
Other Punctuation855
 
2.9%
Dash Punctuation463
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5195
20.1%
r2955
11.4%
e2935
11.4%
s2316
9.0%
n2128
8.2%
i1748
 
6.8%
t1466
 
5.7%
c1118
 
4.3%
u951
 
3.7%
b818
 
3.2%
Other values (8)4189
16.2%
Space Separator
ValueCountFrequency (%)
2683
100.0%
Other Punctuation
ValueCountFrequency (%)
.855
100.0%
Dash Punctuation
ValueCountFrequency (%)
-463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25819
86.6%
Common4001
 
13.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a5195
20.1%
r2955
11.4%
e2935
11.4%
s2316
9.0%
n2128
8.2%
i1748
 
6.8%
t1466
 
5.7%
c1118
 
4.3%
u951
 
3.7%
b818
 
3.2%
Other values (8)4189
16.2%
Common
ValueCountFrequency (%)
2683
67.1%
.855
 
21.4%
-463
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII29820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a5195
17.4%
r2955
9.9%
e2935
9.8%
2683
9.0%
s2316
 
7.8%
n2128
 
7.1%
i1748
 
5.9%
t1466
 
4.9%
c1118
 
3.7%
u951
 
3.2%
Other values (11)6325
21.2%

score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2098
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.471402821
Minimum2.375091791
Maximum8.01893425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:40.818675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.375091791
5-th percentile3.677594018
Q14.652504206
median5.391887426
Q36.28298223
95-th percentile7.377043867
Maximum8.01893425
Range5.643842459
Interquartile range (IQR)1.630478024

Descriptive statistics

Standard deviation1.112681737
Coefficient of variation (CV)0.2033631544
Kurtosis-0.6720955235
Mean5.471402821
Median Absolute Deviation (MAD)0.8010075092
Skewness0.05668185573
Sum11479.00312
Variance1.238060647
MonotonicityNot monotonic
2022-07-23T14:08:40.949862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7235898971
 
< 0.1%
5.4052462581
 
< 0.1%
6.242094041
 
< 0.1%
6.1114850041
 
< 0.1%
6.2012681961
 
< 0.1%
6.1620764731
 
< 0.1%
6.0070219041
 
< 0.1%
5.7502822881
 
< 0.1%
5.7461318971
 
< 0.1%
5.875931741
 
< 0.1%
Other values (2088)2088
99.5%
ValueCountFrequency (%)
2.3750917911
< 0.1%
2.5229001051
< 0.1%
2.661718131
< 0.1%
2.6875529291
< 0.1%
2.6930611131
< 0.1%
2.6935231691
< 0.1%
2.6943032741
< 0.1%
2.7015912531
< 0.1%
2.8078551291
< 0.1%
2.8166224961
< 0.1%
ValueCountFrequency (%)
8.018934251
< 0.1%
7.9708919531
< 0.1%
7.8893499371
< 0.1%
7.858107091
< 0.1%
7.8421001431
< 0.1%
7.8342332841
< 0.1%
7.7882518771
< 0.1%
7.788231851
< 0.1%
7.7803478241
< 0.1%
7.7762088781
< 0.1%

gdp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2052
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.374486027
Minimum6.635322094
Maximum11.64816856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:41.077867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.635322094
5-th percentile7.375650191
Q18.48026371
median9.457890987
Q310.35551786
95-th percentile10.93099761
Maximum11.64816856
Range5.01284647
Interquartile range (IQR)1.875254154

Descriptive statistics

Standard deviation1.148659569
Coefficient of variation (CV)0.1225304049
Kurtosis-0.8417607475
Mean9.374486027
Median Absolute Deviation (MAD)0.9386706352
Skewness-0.3124240913
Sum19667.67168
Variance1.319418805
MonotonicityNot monotonic
2022-07-23T14:08:41.209525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.37448602719
 
0.9%
9.5843744285
 
0.2%
9.0731039055
 
0.2%
10.870995525
 
0.2%
7.5784368524
 
0.2%
8.484533314
 
0.2%
11.000312813
 
0.1%
9.8259859092
 
0.1%
9.1862010962
 
0.1%
7.9257769582
 
0.1%
Other values (2042)2047
97.6%
ValueCountFrequency (%)
6.6353220942
0.1%
6.6782274251
< 0.1%
6.7187623981
< 0.1%
6.7233085631
< 0.1%
6.7281641961
< 0.1%
6.741916181
< 0.1%
6.7481760981
< 0.1%
6.7758231161
< 0.1%
6.785016061
< 0.1%
6.7869830131
< 0.1%
ValueCountFrequency (%)
11.648168561
< 0.1%
11.646564481
< 0.1%
11.644917491
< 0.1%
11.640029911
< 0.1%
11.633571621
< 0.1%
11.616852761
< 0.1%
11.598289491
< 0.1%
11.594555851
< 0.1%
11.591707231
< 0.1%
11.579789161
< 0.1%

social_support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2093
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8127308467
Minimum0.29018417
Maximum0.9873434901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:41.336752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.29018417
5-th percentile0.5689787805
Q10.7499016374
median0.8347702026
Q30.9050675035
95-th percentile0.9509908259
Maximum0.9873434901
Range0.6971593201
Interquartile range (IQR)0.155165866

Descriptive statistics

Standard deviation0.118087546
Coefficient of variation (CV)0.1452972364
Kurtosis1.137096834
Mean0.8127308467
Median Absolute Deviation (MAD)0.07520955801
Skewness-1.099925814
Sum1705.109316
Variance0.01394466852
MonotonicityNot monotonic
2022-07-23T14:08:41.473541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81273084672
 
0.1%
0.69098120932
 
0.1%
0.83813166622
 
0.1%
0.55999594932
 
0.1%
0.80194342142
 
0.1%
0.91193491221
 
< 0.1%
0.93592387441
 
< 0.1%
0.90457862621
 
< 0.1%
0.955065311
 
< 0.1%
0.85102856161
 
< 0.1%
Other values (2083)2083
99.3%
ValueCountFrequency (%)
0.290184171
< 0.1%
0.29093381761
< 0.1%
0.29133367541
< 0.1%
0.3029550911
< 0.1%
0.3195891381
< 0.1%
0.32569253441
< 0.1%
0.3729078771
< 0.1%
0.38237351181
< 0.1%
0.38739091161
< 0.1%
0.41997286681
< 0.1%
ValueCountFrequency (%)
0.98734349011
< 0.1%
0.9849400521
< 0.1%
0.98448896411
< 0.1%
0.98328608271
< 0.1%
0.98293787241
< 0.1%
0.98287373781
< 0.1%
0.98252171281
< 0.1%
0.98182457691
< 0.1%
0.98150175811
< 0.1%
0.98028320071
< 0.1%

hle
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct962
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.47850266
Minimum32.29999924
Maximum77.09999847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:41.609483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum32.29999924
5-th percentile49.2939991
Q159
median65.09999847
Q368.53499985
95-th percentile73
Maximum77.09999847
Range44.79999924
Interquartile range (IQR)9.534999847

Descriptive statistics

Standard deviation7.370184677
Coefficient of variation (CV)0.1161052068
Kurtosis0.02748816822
Mean63.47850266
Median Absolute Deviation (MAD)4.599998474
Skewness-0.747304549
Sum133177.8986
Variance54.31962218
MonotonicityNot monotonic
2022-07-23T14:08:41.748777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.4785026655
 
2.6%
72.1999969516
 
0.8%
7315
 
0.7%
66.4000015314
 
0.7%
67.1999969514
 
0.7%
66.5999984713
 
0.6%
65.512
 
0.6%
72.5999984712
 
0.6%
72.4000015312
 
0.6%
66.8000030512
 
0.6%
Other values (952)1923
91.7%
ValueCountFrequency (%)
32.299999241
< 0.1%
36.860000611
< 0.1%
40.299999241
< 0.1%
40.380001071
< 0.1%
40.808292391
< 0.1%
40.900001531
< 0.1%
41.200000761
< 0.1%
41.419998171
< 0.1%
41.580001831
< 0.1%
42.099998471
< 0.1%
ValueCountFrequency (%)
77.099998471
< 0.1%
76.952857971
< 0.1%
76.820091251
< 0.1%
76.800003051
< 0.1%
76.51
< 0.1%
76.199996951
< 0.1%
75.900001531
< 0.1%
75.680000311
< 0.1%
75.459999081
< 0.1%
75.199996951
< 0.1%

freedom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2084
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7470967404
Minimum0.2575338185
Maximum0.9851777554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:41.879173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2575338185
5-th percentile0.4823555097
Q10.6530638486
median0.768430233
Q30.8614373654
95-th percentile0.9354187995
Maximum0.9851777554
Range0.7276439369
Interquartile range (IQR)0.2083735168

Descriptive statistics

Standard deviation0.1409309638
Coefficient of variation (CV)0.1886381726
Kurtosis-0.08191879678
Mean0.7470967404
Median Absolute Deviation (MAD)0.1037042141
Skewness-0.651114902
Sum1567.408961
Variance0.01986153657
MonotonicityNot monotonic
2022-07-23T14:08:42.012869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8320024615
 
0.2%
0.87668681143
 
0.1%
0.90429270273
 
0.1%
0.55216717722
 
0.1%
0.75527745492
 
0.1%
0.683557572
 
0.1%
0.9243336322
 
0.1%
0.87726259232
 
0.1%
0.95857906342
 
0.1%
0.82964199781
 
< 0.1%
Other values (2074)2074
98.9%
ValueCountFrequency (%)
0.25753381851
< 0.1%
0.26006931071
< 0.1%
0.28145793081
< 0.1%
0.28681439161
< 0.1%
0.29461178181
< 0.1%
0.30354040861
< 0.1%
0.30613189941
< 0.1%
0.31456461551
< 0.1%
0.33243611451
< 0.1%
0.33331209421
< 0.1%
ValueCountFrequency (%)
0.98517775541
< 0.1%
0.98380303381
< 0.1%
0.97993713621
< 0.1%
0.97113502031
< 0.1%
0.97029453521
< 0.1%
0.970130981
< 0.1%
0.96989798551
< 0.1%
0.96978837251
< 0.1%
0.96858048441
< 0.1%
0.96786928181
< 0.1%

generosity
Real number (ℝ)

HIGH CORRELATION

Distinct2017
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002155023477
Minimum-0.3350402415
Maximum0.6980987787
Zeros0
Zeros (%)0.0%
Negative1198
Negative (%)57.1%
Memory size16.5 KiB
2022-07-23T14:08:42.143909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3350402415
5-th percentile-0.2264967509
Q1-0.1155220345
median-0.02770951204
Q30.08837877028
95-th percentile0.2992026806
Maximum0.6980987787
Range1.03313902
Interquartile range (IQR)0.2039008047

Descriptive statistics

Standard deviation0.1601329
Coefficient of variation (CV)-74.30680068
Kurtosis0.9003523515
Mean-0.002155023477
Median Absolute Deviation (MAD)0.1007356476
Skewness0.8222240574
Sum-4.521239255
Variance0.02564254566
MonotonicityNot monotonic
2022-07-23T14:08:42.279372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0277095120420
 
1.0%
0.2184768176
 
0.3%
-0.16875705126
 
0.3%
-0.070494405935
 
0.2%
-0.16252174975
 
0.2%
-0.14671222874
 
0.2%
0.067344248293
 
0.1%
0.060525685552
 
0.1%
0.030108893292
 
0.1%
-0.08113867792
 
0.1%
Other values (2007)2043
97.4%
ValueCountFrequency (%)
-0.33504024151
< 0.1%
-0.31643930081
< 0.1%
-0.30656155941
< 0.1%
-0.30501219631
< 0.1%
-0.30490773921
< 0.1%
-0.3032038511
< 0.1%
-0.30287697911
< 0.1%
-0.29636645321
< 0.1%
-0.29509571191
< 0.1%
-0.29305177931
< 0.1%
ValueCountFrequency (%)
0.69809877871
< 0.1%
0.68931800131
< 0.1%
0.68756020071
< 0.1%
0.67942637211
< 0.1%
0.65000909571
< 0.1%
0.64497506621
< 0.1%
0.56113839151
< 0.1%
0.55534803871
< 0.1%
0.55252140761
< 0.1%
0.54155296091
< 0.1%

corruption
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2006
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7449791153
Minimum0.03519798815
Maximum0.9832760096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:42.410357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.03519798815
5-th percentile0.3208666831
Q10.6935741156
median0.7944760323
Q30.8661777973
95-th percentile0.9394406527
Maximum0.9832760096
Range0.9480780214
Interquartile range (IQR)0.1726036817

Descriptive statistics

Standard deviation0.1825190928
Coefficient of variation (CV)0.244998939
Kurtosis2.080574333
Mean0.7449791153
Median Absolute Deviation (MAD)0.0829115808
Skewness-1.532030244
Sum1562.966184
Variance0.03331321924
MonotonicityNot monotonic
2022-07-23T14:08:42.546924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.744979115378
 
3.7%
0.65298581126
 
0.3%
0.79477751263
 
0.1%
0.85958343742
 
0.1%
0.76053339242
 
0.1%
0.74467271572
 
0.1%
0.80028772352
 
0.1%
0.23746000232
 
0.1%
0.081958577042
 
0.1%
0.80314886572
 
0.1%
Other values (1996)1997
95.2%
ValueCountFrequency (%)
0.035197988151
< 0.1%
0.047311153261
< 0.1%
0.060282066461
< 0.1%
0.063614882531
< 0.1%
0.065775275231
< 0.1%
0.069619603461
< 0.1%
0.078000180421
< 0.1%
0.081324897711
< 0.1%
0.081958577042
0.1%
0.094604469841
< 0.1%
ValueCountFrequency (%)
0.98327600961
< 0.1%
0.98293089871
< 0.1%
0.9788001181
< 0.1%
0.97691738611
< 0.1%
0.9767774941
< 0.1%
0.97633963821
< 0.1%
0.97606104611
< 0.1%
0.97368633751
< 0.1%
0.97273898121
< 0.1%
0.97266858821
< 0.1%

positive_affect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1939
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.709531264
Minimum0.3216897547
Maximum0.9436206222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:42.677012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.3216897547
5-th percentile0.5290372968
Q10.6259798557
median0.721203506
Q30.7967924476
95-th percentile0.8615489691
Maximum0.9436206222
Range0.6219308674
Interquartile range (IQR)0.1708125919

Descriptive statistics

Standard deviation0.1065538581
Coefficient of variation (CV)0.1501750008
Kurtosis-0.467234684
Mean0.709531264
Median Absolute Deviation (MAD)0.08407738805
Skewness-0.3913917137
Sum1488.596592
Variance0.01135372469
MonotonicityNot monotonic
2022-07-23T14:08:42.810863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7095312648
 
0.4%
0.58463388683
 
0.1%
0.58494430783
 
0.1%
0.58718240263
 
0.1%
0.58106738333
 
0.1%
0.74889761212
 
0.1%
0.64791953562
 
0.1%
0.78326988222
 
0.1%
0.70583462722
 
0.1%
0.79666101932
 
0.1%
Other values (1929)2068
98.6%
ValueCountFrequency (%)
0.32168975472
0.1%
0.35138705372
0.1%
0.36249768731
< 0.1%
0.36943960191
< 0.1%
0.38429245352
0.1%
0.38698670271
< 0.1%
0.42096188661
< 0.1%
0.42222747211
< 0.1%
0.42292764781
< 0.1%
0.42412531381
< 0.1%
ValueCountFrequency (%)
0.94362062221
< 0.1%
0.93437367681
< 0.1%
0.92456096411
< 0.1%
0.91893708711
< 0.1%
0.91680097581
< 0.1%
0.91049695011
< 0.1%
0.90617847441
< 0.1%
0.90277212861
< 0.1%
0.90126794581
< 0.1%
0.90066766741
< 0.1%

negative_affect
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1938
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2710992047
Minimum0.08273695409
Maximum0.7045896649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:42.939593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08273695409
5-th percentile0.1513604492
Q10.2081296816
median0.260602951
Q30.3227720782
95-th percentile0.4242400825
Maximum0.7045896649
Range0.6218527108
Interquartile range (IQR)0.1146423966

Descriptive statistics

Standard deviation0.08548124039
Coefficient of variation (CV)0.3153135048
Kurtosis0.7113558718
Mean0.2710992047
Median Absolute Deviation (MAD)0.05562014878
Skewness0.6927269757
Sum568.7661315
Variance0.007307042459
MonotonicityNot monotonic
2022-07-23T14:08:43.072991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2606029518
 
0.4%
0.21519775693
 
0.1%
0.43877434733
 
0.1%
0.25643119223
 
0.1%
0.44038730863
 
0.1%
0.32688900833
 
0.1%
0.43813446162
 
0.1%
0.28560975192
 
0.1%
0.42470666772
 
0.1%
0.32168421152
 
0.1%
Other values (1928)2067
98.5%
ValueCountFrequency (%)
0.082736954092
0.1%
0.083425760271
< 0.1%
0.092695645991
< 0.1%
0.093412384391
< 0.1%
0.094316124921
< 0.1%
0.095490492881
< 0.1%
0.099630385641
< 0.1%
0.10349379481
< 0.1%
0.10615817461
< 0.1%
0.10687077791
< 0.1%
ValueCountFrequency (%)
0.70458966491
< 0.1%
0.64258873461
< 0.1%
0.62222993371
< 0.1%
0.59933549171
< 0.1%
0.59053874021
< 0.1%
0.58126693961
< 0.1%
0.56975805761
< 0.1%
0.56363111731
< 0.1%
0.55709868671
< 0.1%
0.55427873131
< 0.1%

year
Real number (ℝ≥0)

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.768827
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:43.187698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2021
Maximum2021
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.486448695
Coefficient of variation (CV)0.002227886654
Kurtosis-1.07123813
Mean2013.768827
Median Absolute Deviation (MAD)4
Skewness-0.0958651162
Sum4224887
Variance20.1282219
MonotonicityNot monotonic
2022-07-23T14:08:43.293895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2021149
 
7.1%
2017147
 
7.0%
2011146
 
7.0%
2014145
 
6.9%
2019144
 
6.9%
2015143
 
6.8%
2018142
 
6.8%
2016142
 
6.8%
2012142
 
6.8%
2013137
 
6.5%
Other values (7)661
31.5%
ValueCountFrequency (%)
200527
 
1.3%
200689
4.2%
2007102
4.9%
2008110
5.2%
2009114
5.4%
2010124
5.9%
2011146
7.0%
2012142
6.8%
2013137
6.5%
2014145
6.9%
ValueCountFrequency (%)
2021149
7.1%
202095
4.5%
2019144
6.9%
2018142
6.8%
2017147
7.0%
2016142
6.8%
2015143
6.8%
2014145
6.9%
2013137
6.5%
2012142
6.8%

cat_region
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.23164919
Minimum0
Maximum10
Zeros321
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:43.393207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.557646965
Coefficient of variation (CV)0.6800239917
Kurtosis-1.430845831
Mean5.23164919
Median Absolute Deviation (MAD)4
Skewness-0.01141648249
Sum10976
Variance12.65685193
MonotonicityNot monotonic
2022-07-23T14:08:43.487261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9463
22.1%
0321
15.3%
4309
14.7%
10292
13.9%
3196
9.3%
2182
 
8.7%
5171
 
8.2%
656
 
2.7%
146
 
2.2%
732
 
1.5%
ValueCountFrequency (%)
0321
15.3%
146
 
2.2%
2182
 
8.7%
3196
9.3%
4309
14.7%
5171
 
8.2%
656
 
2.7%
732
 
1.5%
830
 
1.4%
9463
22.1%
ValueCountFrequency (%)
10292
13.9%
9463
22.1%
830
 
1.4%
732
 
1.5%
656
 
2.7%
5171
 
8.2%
4309
14.7%
3196
9.3%
2182
 
8.7%
146
 
2.2%

cat_country
Real number (ℝ≥0)

HIGH CORRELATION

Distinct165
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.99809342
Minimum0
Maximum164
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:43.603337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q141
median80
Q3123
95-th percentile157.15
Maximum164
Range164
Interquartile range (IQR)82

Descriptive statistics

Standard deviation47.95015092
Coefficient of variation (CV)0.5847715345
Kurtosis-1.195623023
Mean81.99809342
Median Absolute Deviation (MAD)41
Skewness0.03297796362
Sum172032
Variance2299.216973
MonotonicityNot monotonic
2022-07-23T14:08:43.736459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3219
 
0.9%
14516
 
0.8%
8416
 
0.8%
12416
 
0.8%
15116
 
0.8%
3816
 
0.8%
14716
 
0.8%
4016
 
0.8%
4116
 
0.8%
4216
 
0.8%
Other values (155)1935
92.2%
ValueCountFrequency (%)
013
0.6%
114
0.7%
29
0.4%
34
 
0.2%
416
0.8%
515
0.7%
615
0.7%
714
0.7%
815
0.7%
912
0.6%
ValueCountFrequency (%)
16416
0.8%
16315
0.7%
16213
0.6%
16115
0.7%
16016
0.8%
15914
0.7%
15816
0.8%
15716
0.8%
15616
0.8%
15514
0.7%

rounded_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.45471878
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:43.838149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q36
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133484439
Coefficient of variation (CV)0.2077988774
Kurtosis-0.5502919417
Mean5.45471878
Median Absolute Deviation (MAD)1
Skewness0.06012432493
Sum11444
Variance1.284786974
MonotonicityNot monotonic
2022-07-23T14:08:43.930454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5687
32.7%
6561
26.7%
4371
17.7%
7362
17.3%
361
 
2.9%
855
 
2.6%
21
 
< 0.1%
ValueCountFrequency (%)
21
 
< 0.1%
361
 
2.9%
4371
17.7%
5687
32.7%
6561
26.7%
7362
17.3%
855
 
2.6%
ValueCountFrequency (%)
855
 
2.6%
7362
17.3%
6561
26.7%
5687
32.7%
4371
17.7%
361
 
2.9%
21
 
< 0.1%

scaled_hle
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct962
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6211214666
Minimum0.1757142748
Maximum0.8157142639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2022-07-23T14:08:44.441516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1757142748
5-th percentile0.4184857014
Q10.5571428571
median0.6442856925
Q30.6933571407
95-th percentile0.7571428571
Maximum0.8157142639
Range0.6399999891
Interquartile range (IQR)0.1362142835

Descriptive statistics

Standard deviation0.1052883525
Coefficient of variation (CV)0.1695133049
Kurtosis0.02748816822
Mean0.6211214666
Median Absolute Deviation (MAD)0.06571426392
Skewness-0.747304549
Sum1303.112837
Variance0.01108563718
MonotonicityNot monotonic
2022-07-23T14:08:44.542699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.621121466655
 
2.6%
0.745714242116
 
0.8%
0.757142857115
 
0.7%
0.662857164714
 
0.7%
0.674285670714
 
0.7%
0.665714263913
 
0.6%
0.6512
 
0.6%
0.751428549612
 
0.6%
0.748571450412
 
0.6%
0.668571472212
 
0.6%
Other values (952)1923
91.7%
ValueCountFrequency (%)
0.17571427481
< 0.1%
0.24085715161
< 0.1%
0.28999998911
< 0.1%
0.29114287241
< 0.1%
0.29726131981
< 0.1%
0.29857145041
< 0.1%
0.30285715381
< 0.1%
0.30599997381
< 0.1%
0.30828574041
< 0.1%
0.31571426391
< 0.1%
ValueCountFrequency (%)
0.81571426391
< 0.1%
0.81361225671
< 0.1%
0.81171558931
< 0.1%
0.8114286151
< 0.1%
0.80714285711
< 0.1%
0.80285709931
< 0.1%
0.79857145041
< 0.1%
0.79542857581
< 0.1%
0.79228570121
< 0.1%
0.7885713851
< 0.1%

Interactions

2022-07-23T14:08:38.357991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.165899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.398451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.766528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.523137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.301595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.053129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.910024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.573511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.337060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.305619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.995892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.670679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.374452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.469132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.280668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.481325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.883203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.641469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.424447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.173239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.019176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.694022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.457151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.417401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.114332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.789379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.495199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.585617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.371336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.562862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.002625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.756662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.546905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.288611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.130851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.813679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.576442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.518835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.230652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.905817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.616252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.707686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.460321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.651583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.132680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.880482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.677804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.413221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.249389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.943389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.703473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.634590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.353486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.031778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.749336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.825360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.539794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.845603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.250102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.990817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.795837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.526649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.358864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.059120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.819613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.745891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.466895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.147354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.867795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.949684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.625472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.947997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.378241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.254503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.924646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.650579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.482695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.183516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.945329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.870225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.588657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.275329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.995977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.071564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.708760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.030415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.499940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.361025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.046090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.765917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.598131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.306976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.064783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:31.992579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.705182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.393172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:37.116381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.194331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.796140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.116294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.626585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.470968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.173605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:24.886658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.717166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.454372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.188581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.113480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.824461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.513695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:37.239027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.317033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.883196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.203747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.753617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.584170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.299385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.010831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.844498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.584153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.313576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.240869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:33.947526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.636708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:37.364222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.439917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:16.969687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.289492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:19.884795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.702860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.427457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.131954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:26.968238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.711108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.443467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.368373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.068098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.759611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:37.486963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.565463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.056263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.376090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.017395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.825383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.555741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.256745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.089940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.839977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.573493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.496856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.190980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:35.884478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:37.611196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.685660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.140634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.457167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.140977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:21.941078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.675434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.374476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.207448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:28.961143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.694547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.621425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.308708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.003685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.043635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.809712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.224495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.549105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.266711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.059336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.800535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.496288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.329192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.087120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.817621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.746926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.427805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.124993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.151770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:39.931976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:17.310753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:18.654939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:20.394281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:22.179983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:23.926326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:25.799172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:27.449826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:29.212036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:30.943924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:32.870978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:34.547432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:36.249792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-23T14:08:38.249293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-23T14:08:44.638849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-23T14:08:44.770555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-23T14:08:44.907460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-23T14:08:45.050980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-23T14:08:40.151240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-23T14:08:40.358265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

countryregionscoregdpsocial_supporthlefreedomgenerositycorruptionpositive_affectnegative_affectyearcat_regioncat_countryrounded_scorescaled_hle
0afghanistanasia3.7235907.3701000.45066250.7999990.7181140.1676400.8816860.5176370.2581952008004.00.440000
1afghanistanasia4.4017787.5399720.55230851.2000010.6788960.1900990.8500350.5839260.2370922009004.00.445714
2afghanistanasia4.7583817.6467090.53907551.5999980.6001270.1205900.7067660.6182650.2753242010005.00.451429
3afghanistanasia3.8317197.6195320.52110451.9199980.4959010.1624270.7311090.6113870.2671752011004.00.456000
4afghanistanasia3.7829387.7054790.52063752.2400020.5309350.2360320.7756200.7103850.2679192012004.00.460571
5afghanistanasia3.5721007.7250290.48355252.5600010.5779550.0611480.8232040.6205850.2733282013004.00.465143
6afghanistanasia3.1308967.7183540.52556852.8800010.5085140.1040130.8712420.5316910.3748612014003.00.469714
7afghanistanasia3.9828557.7019920.52859753.2000010.3889280.0798640.8806380.5535530.3392762015004.00.474286
8afghanistanasia4.2201697.6965600.55907253.0000000.5225660.0422650.7932460.5649530.3483322016004.00.471429
9afghanistanasia2.6617187.6973810.49088052.7999990.427011-0.1213030.9543930.4963490.3713262017003.00.468571

Last rows

countryregionscoregdpsocial_supporthlefreedomgenerositycorruptionpositive_affectnegative_affectyearcat_regioncat_countryrounded_scorescaled_hle
2088burundisub-saharan africa3.77536.6353220.49032653.4000020.626350-0.0238760.6069350.6664420.36276720219224.00.477143
2089yemennear east3.65797.5784370.83153757.1215710.602157-0.1467120.8002880.5428060.213043202151624.00.530308
2090tanzaniasub-saharan africa3.62327.8755720.70207157.9989930.8331370.1831500.5769860.6855330.271118202191464.00.542843
2091haitilatin amer. and carib3.61497.4771380.53950455.7000010.5933560.4215200.7210490.5841130.35872020214574.00.510000
2092malawisub-saharan africa3.60006.9575250.53699557.9475250.7797030.0384020.7290650.5366970.34816220219874.00.542108
2093lesothosub-saharan africa3.51187.9257770.78687148.7000010.714954-0.1305360.9153770.7348800.27342620219814.00.410000
2094botswanasub-saharan africa3.46659.7815360.78417159.2691880.824394-0.2461590.8006190.7117960.27272220219183.00.560988
2095rwandasub-saharan africa3.41477.6761180.55233961.3997270.8967580.0605260.1668010.7360680.417668202191233.00.591425
2096zimbabwesub-saharan africa3.14487.9425950.75047056.2008400.676700-0.0473460.8209990.7025730.345736202191643.00.517155
2097afghanistanasia2.52297.6947100.46259652.4926150.381749-0.1016840.9243380.3513870.5024742021003.00.464180